Mihir Shah

Associate Consultant

Greater Toronto Area, Canada2 yrs 2 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Expert in building scalable ML infrastructure.
Strong background in data processing and ETL workflows.
Pursuing MSc in Applied Computing with a focus on AI.

Stackforce AI infers this person is a SaaS-focused engineer with expertise in ML infrastructure and data processing.

Contact

Skills

Core Skills

Software InfrastructureSoftware DesignPython (programming Language)Computer ScienceMlopsBig DataLarge Language Models (llm)Systems DesignContinuous Integration And Continuous Delivery (ci/cd)Machine LearningArtificial Intelligence (ai)

Other Skills

AI AgentsAdobe PhotoshopAmazon Web Services (AWS)AnalyticsApache AirflowApache SparkArtificial Neural NetworksBashC (Programming Language)C++Data AnalysisData AnalyticsData MiningData ScienceData Structures

About

I’m an engineer, wrangling ML models and building ML infrastructure for a living. I have 2+ years of experience in building scalable data platforms and production-ready ML workflows. My work spans cloud infrastructure optimization and designing end-to-end ML pipelines for distributed systems. I’ve contributed to systems involving LLM-based insights, semantic search, and automated data processing at scale. Currently, I’m pursuing my MSc in Applied Computing (AI) at the University of Toronto, where I’m deepening my focus on applied research in computer vision and NLP.

Experience

2 yrs 2 mos

Total Experience

1 yr 11 mos

Average Tenure

Current Experience

Department of computer science, university of toronto

2 roles

Teaching Assistant

Jan 2026 – Present · 3 mos · Greater Toronto Area, Canada

Courses: CSC209H1S - Software Tools and Systems Programming

Software InfrastructureShell ScriptingSoftware DesignC (Programming Language)Machine Code

Teaching Assistant

Sep 2025 – Dec 2025 · 3 mos · Greater Toronto Area, Canada

Course: CSC108H1F - Introduction to Computer Programming

Python (Programming Language)Memory ManagementComputer ScienceUniversity TeachingData Structures

Nielsen

Software Engineer II

Oct 2023 – Sep 2025 · 1 yr 11 mos · Greater Bengaluru Area · Hybrid

Collaborated with the ML team developing an early-stage LLM-based internal product to provide leadership with insights from DAG runs, including performance metrics, runtime trends, and resource utilization.
The system uses VectorDB (ChromaDB), LangChain, and LangSmith for scalable semantic search, metadata management, across large ETL workflows in Airflow.
Converted large-scale pipeline metadata into a FAISS-backed VectorDB, building pipelines to clean, preprocess, and transform structured and unstructured data into embeddings for efficient semantic search and integration with the LLM analytics layer.
Built large-scale cron-based data pipelines with Scala, Spark, and Apache Airflow to process ~31GB of data weekly, including inconsistent data, detecting duplicates, and suggesting normalized.
Built metrics pipeline using workflows to generate user and household-level features (impression counts, computed ad-clicks, viewing frequency, cross-platform reach, ad exposure) powering ML models that measured audience reach and across 5,000+ advertising campaigns.
Managed AWS infrastructure with EMR clusters, deploying memory-optimized instances, and configuring node count, reducing costs by 12%.
Managed and improved in-house service communication with an SQS messaging, and automated alerts for on-call responses to prod failures.
Maintained CI/CD pipelines using Github Actions, with integrated vulnerability checks, builds, and artifact deployments, ensuring seamless and efficient releases.

DockerBig DataMLOpsLarge Language Model Operations (LLMOps)ScalingScripting+12

Sleevesup

Software Engineer

Mar 2023 – Aug 2023 · 5 mos · Greater Bengaluru Area · On-site

Created Scalable online applications using Fastapi, Django and maintained.
Dockerized and deployed scalable online applications using Fastapi and Springboot
Used RabbitMq as message broker for better user experience & Redis for caching which helped the apps pertormance and speed.
Deployed the app using Cl/CD pIpeline constructed in Jenkins into a Kubernetes cluster.
Setup NGINX as Reverse-proxy, Loadbalancer & entrypoint for the backend-server.
Carried out rigorous unit testing and took part in code reviews to guarantee high-quality code and coding standards compliance.
Sophisticated software flaws were looked into and fixed, enhancing user experience and overall application stability and security.
Participated in daily stand-ups, sprint planning, and retrospectives while working in an agile setting.